U.S. patent application number 16/380007 was filed with the patent office on 2020-10-15 for discriminate among and estimate velocities of multiple objects using multi-node radar system.
The applicant listed for this patent is GM Global Technology Operations LLC. Invention is credited to Oded Bialer, Amnon Jonas, David Shapiro.
Application Number | 20200326417 16/380007 |
Document ID | / |
Family ID | 1000004048941 |
Filed Date | 2020-10-15 |
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United States Patent
Application |
20200326417 |
Kind Code |
A1 |
Shapiro; David ; et
al. |
October 15, 2020 |
DISCRIMINATE AMONG AND ESTIMATE VELOCITIES OF MULTIPLE OBJECTS
USING MULTI-NODE RADAR SYSTEM
Abstract
A system and method using a multi-node radar system involve
receiving reflected signals at each node of the multi-node radar
system, the reflected signals resulting from reflection of
transmitted signals by one or more objects, and generating velocity
lines associated with each of the reflected signals received at
each of the nodes, each velocity line being derived from a radial
velocity Vr and an angle of arrival .theta. determined from the
reflected signal received at the node. The method also includes
determining one or more intersection points of the velocity lines,
and estimating a velocity of each of the one or more objects based
on the one or more intersection points. Each intersection point
corresponds with the velocity for one of the one or more objects
and the velocity is a relative velocity vector between the one of
the one or more objects and the radar system.
Inventors: |
Shapiro; David; (Netanya,
IL) ; Bialer; Oded; (Petah Tivak, IL) ; Jonas;
Amnon; (Jerusalem, IL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
GM Global Technology Operations LLC |
Detroit |
MI |
US |
|
|
Family ID: |
1000004048941 |
Appl. No.: |
16/380007 |
Filed: |
April 10, 2019 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01S 13/589 20130101;
G01S 13/931 20130101; G01S 7/417 20130101 |
International
Class: |
G01S 13/58 20060101
G01S013/58; G01S 13/93 20060101 G01S013/93; G01S 7/41 20060101
G01S007/41 |
Claims
1. A method of using a multi-node radar system, the method
comprising: receiving reflected signals at each node of the
multi-node radar system, the reflected signals resulting from
reflection of transmitted signals by one or more objects;
generating, using a processor, velocity lines associated with each
of the reflected signals received at each of the nodes, each
velocity line being derived from a radial velocity Vr and an angle
of arrival .theta. determined from the reflected signal received at
the node; determining, using the processor, one or more
intersection points of the velocity lines; and estimating, using
the processor, a velocity of each of the one or more objects based
on the one or more intersection points, each intersection point
corresponding with the velocity for one of the one or more objects
and the velocity being a relative velocity vector between the one
of the one or more objects and the radar system.
2. The method according to claim 1, wherein the receiving the
reflected signals at each node includes receiving the reflected
signals at one or more receive antenna elements at each node.
3. The method according to claim 1, wherein the generating the
velocity lines includes computing: V y ' = V r cos ( .theta. ) -
tan ( .theta. ) V x ' , ##EQU00004## where Vy' and Vx' are values
on two perpendicular axes.
4. The method according to claim 1, wherein the determining the one
or more intersection points includes determining the intersection
point for each set of intersecting ones of the velocity lines.
5. The method according to claim 1, further comprising training a
neural network to perform the determining the one or more
intersection points.
6. The method according to claim 5, wherein the training the neural
network includes obtaining training reflected signals from a
training radar system or obtaining simulated reflected signals.
7. The method according to claim 6, wherein the training the neural
network includes performing supervised learning by determining
actual intersection points corresponding with each training object
among one or more of the training objects that generate the
training reflected signals or the simulated reflected signals.
8. The method according to claim 7, wherein the training the neural
network includes obtaining training intersection points by using
the neural network on the training reflected signals or the
simulated reflected signals.
9. The method according to claim 8, wherein the training the neural
network includes comparing the actual intersection points with the
training intersection points to provide feedback to the neural
network.
10. The method according to claim 1, further comprising integrating
the multi-node radar system in a vehicle and controlling operation
of the vehicle based on information including the velocity of the
one or more objects.
11. A system including a multi-node radar system, the system
comprising: each node of the multi-node radar system configured to
receive reflected signals, the reflected signals resulting from
reflection of transmitted signals by one or more objects; and a
processor configured to generate velocity lines associated with
each of the reflected signals received at each of the nodes, each
velocity line being derived from a radial velocity Vr and an angle
of arrival .theta. determined from the reflected signal received at
the node, to determine one or more intersection points of the
velocity lines, and to estimate a velocity of each of the one or
more objects based on the one or more intersection points, each
intersection point corresponding with the velocity for one of the
one or more objects and the velocity being a relative velocity
vector between the one of the one or more objects and the radar
system.
12. The system according to claim 11, wherein each node of the
multi-node radar system includes one or more receive antenna
elements.
13. The system according to claim 11, wherein the processor is
configured to generate the velocity lines by computing: V y ' = V r
cos ( .theta. ) - tan ( .theta. ) V x ' , ##EQU00005## where Vy'
and Vx' are values on two perpendicular axes.
14. The system according to claim 11, wherein the processor is
configured to determine the one or more intersection points based
on determining the intersection point for each set of intersecting
ones of the velocity lines.
15. The system according to claim 11, wherein the processor is
configured to implement a neural network to determine the one or
more intersection points.
16. The system according to claim 15, wherein the neural network is
trained based on obtaining training reflected signals from a
training radar system or obtaining simulated reflected signals.
17. The system according to claim 16, wherein the neural network is
trained based on performing supervised learning by determining
actual intersection points corresponding with each training object
among one or more of the training objects that generate the
training reflected signals or the simulated reflected signals.
18. The system according to claim 17, wherein the neural network is
trained based on obtaining training intersection points by using
the neural network on the training reflected signals or the
simulated reflected signals.
19. The system according to claim 18, wherein the neural network is
trained based on comparing the actual intersection points with the
training intersection points to provide feedback to the neural
network.
20. The system according to claim 11, wherein the multi-node radar
system is in a vehicle and operation of the vehicle is controlled
based on information including the velocity of the one or more
objects.
Description
INTRODUCTION
[0001] The subject disclosure relates to discriminating among
multiple objects and estimating their velocities using a multi-node
radar system.
[0002] Radar systems and other sensors are increasingly used in
vehicles (e.g., automobiles, trucks, farm equipment, construction
equipment, automated factories) to obtain information about the
vehicle and its surroundings. A radar system may identify objects
in the path of the vehicle, for example, and facilitate autonomous
or semi-autonomous vehicle operation. The radar system having a
wide field of view (i.e., wide aperture) facilitates obtaining more
information about the surroundings of the vehicle. Thus, an array
of multiple nodes may be used. Each node may include one or more
transmit and receive antenna elements or transceivers. However,
when there are multiple objects at similar ranges to the radar
system, correctly determining the velocity of each of the objects
is challenging. This is because the angle of arrival of reflections
from each of the objects to each of the nodes is different. As
such, associating the reflections at each of the nodes from the
same object is difficult. Accordingly, it is desirable to provide
discrimination among multiple objects and estimate their velocities
using a multi-node radar system.
SUMMARY
[0003] In one exemplary embodiment, a method of using a multi-node
radar system includes receiving reflected signals at each node of
the multi-node radar system, the reflected signals resulting from
reflection of transmitted signals by one or more objects, and
generating velocity lines associated with each of the reflected
signals received at each of the nodes, each velocity line being
derived from a radial velocity Vr and an angle of arrival .theta.
determined from the reflected signal received at the node. The
method also includes determining one or more intersection points of
the velocity lines, and estimating a velocity of each of the one or
more objects based on the one or more intersection points. Each
intersection point corresponds with the velocity for one of the one
or more objects and the velocity is a relative velocity vector
between the one of the one or more objects and the radar
system.
[0004] In addition to one or more of the features described herein,
the receiving the reflected signals at each node includes receiving
the reflected signals at one or more receive antenna elements at
each node.
[0005] In addition to one or more of the features described herein,
the generating the velocity lines includes computing:
V y ' = V r cos ( .theta. ) - tan ( .theta. ) V x ' ,
##EQU00001##
where Vy' and Vx' are values on two perpendicular axes.
[0006] In addition to one or more of the features described herein,
the determining the one or more intersection points includes
determining the intersection point for each set of intersecting
ones of the velocity lines.
[0007] In addition to one or more of the features described herein,
the method also includes training a neural network to perform the
determining the one or more intersection points.
[0008] In addition to one or more of the features described herein,
the training the neural network includes obtaining training
reflected signals from a training radar system or obtaining
simulated reflected signals.
[0009] In addition to one or more of the features described herein,
the training the neural network includes performing supervised
learning by determining actual intersection points corresponding
with each training object among one or more of the training objects
that generate the training reflected signals or the simulated
reflected signals.
[0010] In addition to one or more of the features described herein,
the training the neural network includes obtaining training
intersection points by using the neural network on the training
reflected signals or the simulated reflected signals.
[0011] In addition to one or more of the features described herein,
the training the neural network includes comparing the actual
intersection points with the training intersection points to
provide feedback to the neural network.
[0012] In addition to one or more of the features described herein,
the method also includes integrating the multi-node radar system in
a vehicle and controlling operation of the vehicle based on
information including the velocity of the one or more objects.
[0013] In another exemplary embodiment, a system including a
multi-node radar system includes each node of the multi-node radar
system to receive reflected signals, the reflected signals
resulting from reflection of transmitted signals by one or more
objects. The system also includes a processor to generate velocity
lines associated with each of the reflected signals received at
each of the nodes. Each velocity line is derived from a radial
velocity Vr and an angle of arrival .theta. determined from the
reflected signal received at the node. The processor also
determines one or more intersection points of the velocity lines,
and estimates a velocity of each of the one or more objects based
on the one or more intersection points. Each intersection point
corresponds with the velocity for one of the one or more objects
and the velocity is a relative velocity vector between the one of
the one or more objects and the radar system.
[0014] In addition to one or more of the features described herein,
each node of the multi-node radar system includes one or more
receive antenna elements.
[0015] In addition to one or more of the features described herein,
the processor generates the velocity lines by computing:
V y ' = V r cos ( .theta. ) - tan ( .theta. ) V x ' ,
##EQU00002##
where Vy' and Vx' are values on two perpendicular axes.
[0016] In addition to one or more of the features described herein,
the processor determines the one or more intersection points based
on determining the intersection point for each set of intersecting
ones of the velocity lines.
[0017] In addition to one or more of the features described herein,
the processor implements a neural network to determine the one or
more intersection points.
[0018] In addition to one or more of the features described herein,
the neural network is trained based on obtaining training reflected
signals from a training radar system or obtaining simulated
reflected signals.
[0019] In addition to one or more of the features described herein,
the neural network is trained based on performing supervised
learning by determining actual intersection points corresponding
with each training object among one or more of the training objects
that generate the training reflected signals or the simulated
reflected signals.
[0020] In addition to one or more of the features described herein,
the neural network is trained based on obtaining training
intersection points by using the neural network on the training
reflected signals or the simulated reflected signals.
[0021] In addition to one or more of the features described herein,
the neural network is trained based on comparing the actual
intersection points with the training intersection points to
provide feedback to the neural network.
[0022] In addition to one or more of the features described herein,
the multi-node radar system is in a vehicle and operation of the
vehicle is controlled based on information including the velocity
of the one or more objects.
[0023] The above features and advantages, and other features and
advantages of the disclosure are readily apparent from the
following detailed description when taken in connection with the
accompanying drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0024] Other features, advantages and details appear, by way of
example only, in the following detailed description, the detailed
description referring to the drawings in which:
[0025] FIG. 1 is a block diagram of a vehicle with a radar system
that discriminates among multiple objects and estimates their
velocities;
[0026] FIG. 2 shows aspects of a radar system that is configured to
discriminate among multiple objects and estimate their velocities
according to one or more embodiments;
[0027] FIG. 3 illustrates an approach to discriminate among
multiple objects and estimate their velocities according to one or
more embodiments;
[0028] FIG. 4 shows the extension of the approach shown in FIG. 3
to the four exemplary objects shown in FIG. 2;
[0029] FIG. 5 is a process flow of a method of training a neural
network to discriminate among multiple objects and estimate their
velocities using a multi-node radar system according to one or more
embodiments; and
[0030] FIG. 6 is a process flow of a method of discriminating among
multiple objects and estimating their velocities using a multi-node
radar system according to one or more embodiments.
DETAILED DESCRIPTION
[0031] The following description is merely exemplary in nature and
is not intended to limit the present disclosure, its application or
uses. It should be understood that throughout the drawings,
corresponding reference numerals indicate like or corresponding
parts and features.
[0032] As previously noted, a multi-node radar system expands the
field of view but presents challenges in a scenario involving
multiple objects at similar ranges from the radar system. This is
because the reflections from each of the objects appear at a
different angle of arrival at each of the nodes. Further, there may
be an overlap in the angle of arrival determined for different
objects at the different nodes. As such, the information obtained
at all of the nodes is difficult to parse in order to identify each
of the multiple objects and estimate each of their velocities.
Velocity refers to the relative velocity between the object and the
radar system. Embodiments of the systems and methods detailed
herein relate to discriminating among multiple objects and
estimating their velocities using a multi-node radar system.
Specifically, the radial velocity estimated at each node for each
object is represented as a velocity line. Intersection points of
the velocity lines are used to discriminate among the multiple
objects and estimate their velocities. Identification of the
intersection points is performed by a neural network that is
trained based on supervised learning.
[0033] In accordance with an exemplary embodiment, FIG. 1 is a
block diagram of a vehicle 100 with a radar system 110 that
discriminates among multiple objects 140 and estimates their
velocities. As previously noted, the velocity that is estimated is
the relative velocity between a given object 140 and the radar
system 110. The radar system 110, which may be in the vehicle 100
as in the example shown in FIG. 1, may be moving or stationary. The
exemplary vehicle 100 shown in FIG. 1 is an automobile 101. The
vehicle 100 may include other sensors 130 (e.g., lidar system,
camera) in addition to the radar system 110. A controller 120 may
control aspects of the operation of the vehicle 100 based on
information obtained from the radar system 110 alone or in
combination with other sensors 130. The radar system 110 transmits
transmit signals 112 and receives reflected signals 115 when
objects 140 in the field of view of the radar system 110 reflect
the transmit signals 112. The exemplary object 140 shown in FIG. 1
is a pedestrian 145.
[0034] The reflected signals 115 may be processed within the radar
system 110, by the controller 120, or a combination of the two.
Whether in the radar system 110 or the controller 120, the
processing involves processing circuitry that may include an
application specific integrated circuit (ASIC), an electronic
circuit, a processor (shared, dedicated, or group) and memory that
executes one or more software or firmware programs, a combinational
logic circuit, and/or other suitable components that provide the
described functionality. As further detailed, the processing
facilitates discrimination among multiple objects 140 with
overlapping ranges and Doppler frequencies that are in the field of
view of the radar system 110.
[0035] FIG. 2 shows aspects of a radar system 110 that is
configured to discriminate among multiple objects and estimate
their velocities according to one or more embodiments. The radar
system 110 includes a number (e.g., ten) of nodes 210. Each node
210 includes one or more transmit elements 203 and one or more
receive elements 205, or, according to alternate embodiments, one
or more transceiver elements that both emit the transmit signals
112 and receive reflected signals 115. Four objects 140 are shown
in FIG. 2. The exemplary objects 140 are cars 220a, 220b, 220c,
220d (generally referred to as 220). The velocity vector V is
indicated for each car 220. In the case of the car 220a, the
lateral and longitudinal components Vx and Vy of the velocity
vector V are also indicated. Each of the cars 220 provides
reflected signals 115 to each of the nodes 210, and these reflected
signals 115 include velocity projections 215. Each velocity
projection 215 is a projection of the corresponding velocity vector
Vin the radial axis to the given node 210. That is, the velocity
projection 215 to a given node 210 indicates the radial velocity Vr
(i.e., Doppler measurement) at that node 210. For a given car 220,
the radial velocity determined at each of the nodes 210 is unlikely
to be the same. Further, when the results at all the nodes 210 are
considered together, the radial velocities of different ones of the
cars 220 are likely to be similar. This issue created by the
overlapping ranges and Doppler frequencies of the cars 220 is
addressed by processing the reflected signals 115 according to one
or more embodiments.
[0036] FIG. 3, with continuing reference to FIGS. 1 and 2,
illustrates an approach to discriminate among multiple objects 140
and to estimate their velocities according to one or more
embodiments. One object 140 and a radar system 110 with three nodes
210-1, 210-2, 210-3 (generally 210) are shown in FIG. 3 for
explanatory purposes. The velocity vector V for the object 140 is
indicated along with the lateral and longitudinal components Vx and
Vy. The velocity projection 215-1, 215-2, 215-3 (generally referred
to as 215) associated with the reflected signal 115 to each node
210 is indicated. As previously noted, the velocity projection 215
indicates the corresponding radial velocity Vr detected for the
object 140 at the node 210. The angles of arrival .theta..sub.1,
.theta..sub.2, .theta..sub.3 (generally .theta.) of the reflected
signals 115 to respective nodes 210 are indicated, as well.
[0037] An image 300 including the velocity lines 310-1, 310-2,
310-3 (generally referred to as 310) respectively associated with
the nodes 210-1, 210-2, 210-3 is shown. Each velocity line 310 is
given by:
V y ' = V r cos ( .theta. ) - tan ( .theta. ) V x ' , [ EQ . 1 ]
##EQU00003##
EQ. 1 includes the lateral and longitudinal components Vx' and Vy'
of the radial velocity Vr determined at each node 210. The
determination of the radial velocity Vr and the angle of arrival
.theta. at each node 210 may be performed within the radar system
110 or by the controller 120 or by a combination of the two. The
determination of the radial velocity Vr and the angle of arrival
.theta. at each node 210 results from standard processing that
implements fast Fourier transforms (FFTs) and beamforming. The
intersection 320 of the velocity lines 310 is an estimate of the
true velocity vector V of the object 140. That is, the lateral and
longitudinal components Vx and Vy that correspond with the
intersection 320 are used to estimate the velocity vector V of the
object 140. This velocity vector Vindicates the relative velocity
of the object 140 with respect to the radar system 110. When each
node 210 receives reflected signals 115 from multiple objects 140,
as in the example illustrated in FIG. 2, multiple intersections
320, each corresponding to the estimate of the velocity vector V of
one of the objects 140, are determined, as further discussed with
reference to FIG. 4.
[0038] FIG. 4, with continuing reference to FIGS. 1-3, shows the
extension of the approach shown in FIG. 3 to the four exemplary
objects 140 shown in FIG. 2. The lateral and longitudinal
components Vx and Vy are indicated in kilometers per hour (kph). An
image 400 of all the velocity lines 310 obtained for all the nodes
210 is shown. The intersection 320 associated with each set of
velocity lines 310, which correspond with each object 140, is
indicated along with the true intersection 410 associated with the
velocity vector V of the corresponding object 140. As detailed with
reference to FIG. 5, a neural network is used to identify the
intersection 410 for each set of velocity lines 310.
[0039] FIG. 5, with continuing reference to FIGS. 1-4, is a process
flow 500 of a method of training a neural network to discriminate
among multiple objects and estimate their velocities using a
multi-node radar system according to one or more embodiments. The
neural network is trained to determine an intersection 320 for each
set of velocity lines 310, as discussed with reference to FIGS. 3
and 4. The training of the neural network is supervised, meaning
that the true intersection 410 (i.e., ground truth), as discussed
with reference to FIG. 4, is provided as part of the training. At
block 510, obtaining velocity ground truth refers to using
simulations or real recorded data to determine an intersection 410
corresponding with one or more objects 140.
[0040] At block 520, the process flow 500 includes obtaining actual
reflected signals 115 or simulated reflected signals 115. If
simulations are used to generate the ground truth (at block 510),
the reflected signals 115 that are part of those simulations may be
used at block 520. At block 530, obtaining radial velocities Vr and
angles of arrival .theta. refers to performing standard processing
on the reflected signals 115 that are obtained at block 520.
Generating velocity lines 310, at block 540, includes using the
radial velocity Vr and angle of arrival .theta. at each node 210
(obtained at block 530) in EQ. 1. At block 550, the process flow
500 includes generating an image 300, 400 of the velocity lines 310
generated for all of the nodes 210 (at block 540).
[0041] At block 560, the neural network uses the image 300, 400 of
the velocity lines 310 to provide an intersection 320 associated
with each set of velocity lines 310. Each set of velocity lines 310
corresponds with one object 140 that provided reflected signals 115
based on transmissions 112 from the radar system 110. The one or
more intersections 320 from the neural network (at block 560) and
the true intersections 410 (from block 510) are provided for a
determination of loss, at block 570. The output from block 570
provides feedback to the neural network, at block 560. The loss
determination may be based on an L1-norm loss function or L2-norm
loss function, for example. The neural network, at block 560, is
trained according to the process flow 500 based on a number of
obtained or simulated data sets corresponding with different
numbers of objects 140 with different velocity vectors V.
[0042] FIG. 6, with continuing reference to FIGS. 1-4, is a process
flow 600 of a method of discriminating among multiple objects and
estimating their velocities using a multi-node radar system
according to one or more embodiments. Once the neural network at
block 560 is trained according to the discussion with reference to
FIG. 5, it can be applied at block 640. At block 610, receiving
reflected signals 115 and obtaining radial velocities Vr and angles
of arrival .theta. refers to using any number of nodes 210 to
receive reflected signals 115 and performing standard processing
(e.g., FFTs, beamforming). Generating velocity lines 310, at block
620, refers to using EQ. 1 for each radial velocity Vr and angle of
arrival .theta. determined at every node 210 block 610. Generating
an image 300, 400 of the velocity lines 310, at block 630,
facilitates using the trained neural network, at block 640, to
identify an intersection 320 for each set of velocity lines 310. At
block 650, each intersection 320 output by the neural network (at
block 640), may be converted to a velocity vector V. That is, each
Vx and Vy pair indicated by each intersection corresponds with a
velocity vector V.
[0043] While the above disclosure has been described with reference
to exemplary embodiments, it will be understood by those skilled in
the art that various changes may be made and equivalents may be
substituted for elements thereof without departing from its scope.
In addition, many modifications may be made to adapt a particular
situation or material to the teachings of the disclosure without
departing from the essential scope thereof. Therefore, it is
intended that the present disclosure not be limited to the
particular embodiments disclosed, but will include all embodiments
falling within the scope thereof
* * * * *